RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1

Abstract

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km2) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km2). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control.

Satellite data reveal how Sudd wetland dynamics are linked with globally-significant methane emissions

“…Generally, optical imagery is limited by cloud cover. As such, radar imaging systems, which are unaffected by cloud cover, represent important sources of information for mapping wetlands at a high temporal and spatial resolution (Hardy et al 2019, Oakes et al 2023 despite the relatively short length of archive. A hybrid approach that combines optical and radar sources represents a promising direction for future research, particularly as routine acquisition of L-band imagery becomes available (a joint NASA-Indian Space Research Organisation system is due to be launched in January 2024), giving greater canopy penetration and estimates of inundated vegetation in complex landscapes.…”

“…But, at spatial resolutions greater than 1 km, these data are unable to resolve hydrological features that underpin wetland system dynamics (di Vittorio and Georgakakos 2021). Inundated herbaceous vegetation can also be mapped using satellite radar imagery at much finer resolutions, typically less than 50 m (Tsyganskaya et al 2018, Hardy et al 2020, Oakes et al 2023. However, satellite radar imaging archives are typically shorter than 10 years in length so that inundation dynamics can only be characterised over relatively short periods.…”

User-Relevant Land Cover Products for Informed Decision-Making in the Complex Terrain of the Peruvian Andes

“…Current solutions to map LULC and changes to it rely on the use of different sensors, including optical (e.g., Sentinel-2, Landsat [14,15]), Synthetic Aperture Radar (SAR) (e.g., Sentinel-1 [16][17][18]), or a combination of both types [8,19,20]. More recently, programs like Norway's International Climate and Forests Initiative (NICFI) [21] opened very highresolution data, namely from PLANET to a wider audience, thus enabling a new set of applications.…”